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Clips tensor values to a maximum L2-norm.

tf.clip_by_norm( t, clip_norm, axes=None, name=None )

Given a tensor `t`

, and a maximum clip value `clip_norm`

, this operation normalizes `t`

so that its L2-norm is less than or equal to `clip_norm`

, along the dimensions given in `axes`

. Specifically, in the default case where all dimensions are used for calculation, if the L2-norm of `t`

is already less than or equal to `clip_norm`

, then `t`

is not modified. If the L2-norm is greater than `clip_norm`

, then this operation returns a tensor of the same type and shape as `t`

with its values set to:

`t * clip_norm / l2norm(t)`

In this case, the L2-norm of the output tensor is `clip_norm`

.

As another example, if `t`

is a matrix and `axes == [1]`

, then each row of the output will have L2-norm less than or equal to `clip_norm`

. If `axes == [0]`

instead, each column of the output will be clipped.

some_nums = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.float32) tf.clip_by_norm(some_nums, 2.0).numpy() array([[0.26967996, 0.5393599 , 0.80903983, 1.0787199 , 1.3483998 ]], dtype=float32)

This operation is typically used to clip gradients before applying them with an optimizer. Most gradient data is a collection of different shaped tensors for different parts of the model. Thus, this is a common usage:

# Get your gradients after training loss_value, grads = grad(model, features, labels) # Apply some clipping grads = [tf.clip_by_norm(g, norm) for g in grads] # Continue on with training optimizer.apply_gradients(grads)

Args | |
---|---|

`t` | A `Tensor` or `IndexedSlices` . This must be a floating point type. |

`clip_norm` | A 0-D (scalar) `Tensor` > 0. A maximum clipping value, also floating point |

`axes` | A 1-D (vector) `Tensor` of type int32 containing the dimensions to use for computing the L2-norm. If `None` (the default), uses all dimensions. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A clipped `Tensor` or `IndexedSlices` . |

Raises | |
---|---|

`ValueError` | If the clip_norm tensor is not a 0-D scalar tensor. |

`TypeError` | If dtype of the input is not a floating point or complex type. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/clip_by_norm